def test_merge_tables_duplicate_column_names():
"""
Confirm tables can be merged with overlapping column names, as long as they're not
included in the list of columns to retain.
"""
d = {
'building_id': [1, 2, 3, 4],
'value': [4, 4, 4, 4],
'dupe': [1, 1, 1, 1]
}
buildings = pd.DataFrame(d).set_index('building_id')
d = {
'household_id': [1, 2, 3],
'building_id': [2, 3, 4],
'dupe': [1, 1, 1]
}
households = pd.DataFrame(d).set_index('household_id')
# Duplicate columns should raise a ValueError
try:
merged = merge_tables([households, buildings])
pytest.fail()
except ValueError as e:
print(e)
# Excluding the duplicated name should make things ok
merged = merge_tables([households, buildings], columns=['value'])
assert sorted(all_cols(merged)) == sorted(['household_id', 'value'])
def test_merge_two_tables():
"""
Merge two tables.
"""
d = {'building_id': [1, 2, 3, 4], 'value': [4, 4, 4, 4]}
buildings = pd.DataFrame(d).set_index('building_id')
d = {'household_id': [1, 2, 3], 'building_id': [2, 3, 4]}
households = pd.DataFrame(d).set_index('household_id')
merged = merge_tables([households, buildings])
assert sorted(all_cols(merged)) == sorted(
['household_id', 'building_id', 'value'])
def test_merge_three_tables_out_of_order():
"""
Merge three tables, where the second and third are each merged onto the first.
"""
d = {'zone_id': [1], 'size': [1]}
zones = pd.DataFrame(d).set_index('zone_id')
d = {'building_id': [1, 2, 3, 4], 'height': [4, 4, 4, 4]}
buildings = pd.DataFrame(d).set_index('building_id')
d = {
'household_id': [1, 2, 3],
'building_id': [2, 3, 4],
'zone_id': [1, 1, 1]
}
households = pd.DataFrame(d).set_index('household_id')
merged = merge_tables([households, buildings, zones])
assert sorted(all_cols(merged)) == sorted(
['household_id', 'building_id', 'zone_id', 'height', 'size'])
def test_merge_three_tables():
"""
Merge three tables.
"""
d = {'zone_id': [1], 'size': [1]}
zones = pd.DataFrame(d).set_index('zone_id')
d = {
'building_id': [1, 2, 3, 4],
'zone_id': [1, 1, 1, 1],
'height': [4, 4, 4, 4]
}
buildings = pd.DataFrame(d).set_index('building_id')
d = {'household_id': [1, 2, 3], 'building_id': [2, 3, 4]}
households = pd.DataFrame(d).set_index('household_id')
merged = merge_tables([households, buildings, zones])
assert sorted(all_cols(merged)) == sorted(
['household_id', 'building_id', 'zone_id', 'height', 'size'])
def test_merge_tables_multiindex():
"""
Merge tables where the source table has a multi-index.
"""
d = {
'building_id': [1, 1, 2, 2],
'unit_id': [1, 2, 1, 2],
'value': [4, 4, 4, 4]
}
units = pd.DataFrame(d).set_index(['building_id', 'unit_id'])
d = {
'household_id': [1, 2, 3],
'building_id': [1, 1, 2],
'unit_id': [1, 2, 1]
}
households = pd.DataFrame(d).set_index('household_id')
merged = merge_tables([households, units])
assert sorted(all_cols(merged)) == sorted(
['household_id', 'building_id', 'unit_id', 'value'])
def test_merge_tables_missing_values():
"""
If the target table includes identifiers not found in the source table, missing
values should be inserted, changing the data type.
"""
d = {
'building_id': [1, 1, 2, 2],
'unit_id': [1, 2, 1, 2],
'value': [4, 4, 4, 4]
}
units = pd.DataFrame(d).set_index(['building_id', 'unit_id'])
d = {
'household_id': [1, 2, 3],
'building_id': [1, 1, 3],
'unit_id': [1, 2, 1]
}
households = pd.DataFrame(d).set_index('household_id')
merged = merge_tables([households, units])
assert units.value.dtype == 'int64'
assert merged.values.dtype == 'float64'